Overview

Dataset statistics

Number of variables13
Number of observations682
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory379.5 KiB
Average record size in memory569.8 B

Variable types

NUM7
CAT6

Warnings

Measure Start Date has constant value "682" Constant
Measure End Date has constant value "682" Constant
Location 1 has constant value "682" Constant
Avg Spending Per Episode (State) is highly correlated with Avg Spending Per Episode (Hospital) and 4 other fieldsHigh correlation
Avg Spending Per Episode (Hospital) is highly correlated with Avg Spending Per Episode (State) and 4 other fieldsHigh correlation
Avg Spending Per Episode (Nation) is highly correlated with Avg Spending Per Episode (Hospital) and 4 other fieldsHigh correlation
Percent of Spending (Hospital) is highly correlated with Avg Spending Per Episode (Hospital) and 4 other fieldsHigh correlation
Percent of Spending (State) is highly correlated with Avg Spending Per Episode (Hospital) and 4 other fieldsHigh correlation
Percent of Spending (Nation) is highly correlated with Avg Spending Per Episode (Hospital) and 4 other fieldsHigh correlation
Hospital Name is uniformly distributed Uniform
Avg Spending Per Episode (Hospital) has 198 (29.0%) zeros Zeros
Avg Spending Per Episode (State) has 124 (18.2%) zeros Zeros
Avg Spending Per Episode (Nation) has 124 (18.2%) zeros Zeros
Percent of Spending (Hospital) has 201 (29.5%) zeros Zeros
Percent of Spending (State) has 155 (22.7%) zeros Zeros
Percent of Spending (Nation) has 155 (22.7%) zeros Zeros

Reproduction

Analysis started2020-12-12 22:08:48.184848
Analysis finished2020-12-12 22:08:52.580630
Duration4.4 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Hospital Name
Categorical

UNIFORM

Distinct31
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
UTAH VALLEY REGIONAL MEDICAL CENTER
 
22
TIMPANOGOS REGIONAL HOSPITAL
 
22
THE ORTHOPEDIC SPECIALTY HOSPITAL
 
22
MOUNTAIN VIEW HOSPITAL
 
22
LOGAN REGIONAL HOSPITAL
 
22
Other values (26)
572 
ValueCountFrequency (%) 
UTAH VALLEY REGIONAL MEDICAL CENTER223.2%
 
TIMPANOGOS REGIONAL HOSPITAL223.2%
 
THE ORTHOPEDIC SPECIALTY HOSPITAL223.2%
 
MOUNTAIN VIEW HOSPITAL223.2%
 
LOGAN REGIONAL HOSPITAL223.2%
 
OGDEN REGIONAL MEDICAL CENTER223.2%
 
CASTLEVIEW HOSPITAL223.2%
 
RIVERTON HOSPITAL223.2%
 
DAVIS HOSPITAL AND MEDICAL CENTER223.2%
 
SALT LAKE REGIONAL MEDICAL CENTER223.2%
 
CACHE VALLEY HOSPITAL223.2%
 
ALTA VIEW HOSPITAL223.2%
 
PARK CITY MEDICAL CENTER223.2%
 
UNIVERSITY HEALTH CARE/UNIV HOSPITALS AND CLINICS223.2%
 
LAKEVIEW HOSPITAL223.2%
 
UINTAH BASIN MEDICAL CENTER223.2%
 
LDS HOSPITAL223.2%
 
BEAR RIVER VALLEY HOSPITAL223.2%
 
SEVIER VALLEY MEDICAL CENTER223.2%
 
AMERICAN FORK HOSPITAL223.2%
 
JORDAN VALLEY MEDICAL CENTER223.2%
 
DIXIE REGIONAL MEDICAL CENTER223.2%
 
MCKAY DEE HOSPITAL223.2%
 
MOUNTAIN WEST MEDICAL CENTER223.2%
 
BEAVER VALLEY HOSPITAL223.2%
 
Other values (6)13219.4%
 
2020-12-12T17:08:52.642184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T17:08:52.719751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length26
Mean length25.80645161
Min length12

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
E187010.6%
 
176010.0%
 
A17389.9%
 
I15628.9%
 
L14528.2%
 
T11666.6%
 
N9025.1%
 
O8805.0%
 
R8584.9%
 
C8584.9%
 
S7484.2%
 
H6163.5%
 
M5503.1%
 
D5062.9%
 
P5062.9%
 
V4182.4%
 
Y3081.8%
 
G2641.5%
 
U1761.0%
 
K1320.8%
 
W1320.8%
 
B880.5%
 
F440.2%
 
J220.1%
 
X220.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1581889.9%
 
Space Separator176010.0%
 
Other Punctuation220.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E187011.8%
 
A173811.0%
 
I15629.9%
 
L14529.2%
 
T11667.4%
 
N9025.7%
 
O8805.6%
 
R8585.4%
 
C8585.4%
 
S7484.7%
 
H6163.9%
 
M5503.5%
 
D5063.2%
 
P5063.2%
 
V4182.6%
 
Y3081.9%
 
G2641.7%
 
U1761.1%
 
K1320.8%
 
W1320.8%
 
B880.6%
 
F440.3%
 
J220.1%
 
X220.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1760100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/22100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1581889.9%
 
Common178210.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E187011.8%
 
A173811.0%
 
I15629.9%
 
L14529.2%
 
T11667.4%
 
N9025.7%
 
O8805.6%
 
R8585.4%
 
C8585.4%
 
S7484.7%
 
H6163.9%
 
M5503.5%
 
D5063.2%
 
P5063.2%
 
V4182.6%
 
Y3081.9%
 
G2641.7%
 
U1761.1%
 
K1320.8%
 
W1320.8%
 
B880.6%
 
F440.3%
 
J220.1%
 
X220.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
176098.8%
 
/221.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII17600100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
E187010.6%
 
176010.0%
 
A17389.9%
 
I15628.9%
 
L14528.2%
 
T11666.6%
 
N9025.1%
 
O8805.0%
 
R8584.9%
 
C8584.9%
 
S7484.2%
 
H6163.5%
 
M5503.1%
 
D5062.9%
 
P5062.9%
 
V4182.4%
 
Y3081.8%
 
G2641.5%
 
U1761.0%
 
K1320.8%
 
W1320.8%
 
B880.5%
 
F440.2%
 
J220.1%
 
X220.1%
 

Provider Number
Real number (ℝ≥0)

Distinct31
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean460026.9677
Minimum460001
Maximum460058
Zeros0
Zeros (%)0.0%
Memory size5.5 KiB
2020-12-12T17:08:52.790812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum460001
5-th percentile460003
Q1460010
median460023
Q3460044
95-th percentile460057
Maximum460058
Range57
Interquartile range (IQR)34

Descriptive statistics

Standard deviation18.27579907
Coefficient of variation (CV)3.972766892e-05
Kurtosis-1.377032568
Mean460026.9677
Median Absolute Deviation (MAD)16
Skewness0.2351795782
Sum313738392
Variance334.0048316
MonotocityNot monotonic
2020-12-12T17:08:52.858370image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
460058223.2%
 
460021223.2%
 
460003223.2%
 
460004223.2%
 
460005223.2%
 
460006223.2%
 
460007223.2%
 
460009223.2%
 
460010223.2%
 
460011223.2%
 
460013223.2%
 
460014223.2%
 
460015223.2%
 
460017223.2%
 
460019223.2%
 
460023223.2%
 
460057223.2%
 
460026223.2%
 
460030223.2%
 
460033223.2%
 
460035223.2%
 
460039223.2%
 
460041223.2%
 
460042223.2%
 
460044223.2%
 
Other values (6)13219.4%
 
ValueCountFrequency (%) 
460001223.2%
 
460003223.2%
 
460004223.2%
 
460005223.2%
 
460006223.2%
 
460007223.2%
 
460009223.2%
 
460010223.2%
 
460011223.2%
 
460013223.2%
 
ValueCountFrequency (%) 
460058223.2%
 
460057223.2%
 
460054223.2%
 
460052223.2%
 
460051223.2%
 
460049223.2%
 
460047223.2%
 
460044223.2%
 
460042223.2%
 
460041223.2%
 

Period
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
1 to 3 days Prior to Index Hospital Admission
217 
1 through 30 days After Discharge from Index Hospital Admission
217 
During Index Hospital Admission
217 
Complete Episode
31 
ValueCountFrequency (%) 
1 to 3 days Prior to Index Hospital Admission21731.8%
 
1 through 30 days After Discharge from Index Hospital Admission21731.8%
 
During Index Hospital Admission21731.8%
 
Complete Episode314.5%
 
2020-12-12T17:08:52.932934image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T17:08:52.978974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:53.038525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length63
Median length45
Mean length44.95454545
Min length16

Overview of Unicode Properties

Unique unicode characters30
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
437114.3%
 
s26358.6%
 
i26358.6%
 
o24498.0%
 
d17675.8%
 
t15505.1%
 
r15195.0%
 
n15195.0%
 
a13024.2%
 
e11783.8%
 
m8992.9%
 
A8682.8%
 
p7132.3%
 
l6822.2%
 
h6512.1%
 
g6512.1%
 
I6512.1%
 
x6512.1%
 
H6512.1%
 
14341.4%
 
u4341.4%
 
34341.4%
 
y4341.4%
 
f4341.4%
 
D4341.4%
 
Other values (5)7132.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2232072.8%
 
Space Separator437114.3%
 
Uppercase Letter28839.4%
 
Decimal Number10853.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
143440.0%
 
343440.0%
 
021720.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
4371100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
s263511.8%
 
i263511.8%
 
o244911.0%
 
d17677.9%
 
t15506.9%
 
r15196.8%
 
n15196.8%
 
a13025.8%
 
e11785.3%
 
m8994.0%
 
p7133.2%
 
l6823.1%
 
h6512.9%
 
g6512.9%
 
x6512.9%
 
u4341.9%
 
y4341.9%
 
f4341.9%
 
c2171.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A86830.1%
 
I65122.6%
 
H65122.6%
 
D43415.1%
 
P2177.5%
 
C311.1%
 
E311.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2520382.2%
 
Common545617.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
437180.1%
 
14348.0%
 
34348.0%
 
02174.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
s263510.5%
 
i263510.5%
 
o24499.7%
 
d17677.0%
 
t15506.2%
 
r15196.0%
 
n15196.0%
 
a13025.2%
 
e11784.7%
 
m8993.6%
 
A8683.4%
 
p7132.8%
 
l6822.7%
 
h6512.6%
 
g6512.6%
 
I6512.6%
 
x6512.6%
 
H6512.6%
 
u4341.7%
 
y4341.7%
 
f4341.7%
 
D4341.7%
 
c2170.9%
 
P2170.9%
 
C310.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII30659100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
437114.3%
 
s26358.6%
 
i26358.6%
 
o24498.0%
 
d17675.8%
 
t15505.1%
 
r15195.0%
 
n15195.0%
 
a13024.2%
 
e11783.8%
 
m8992.9%
 
A8682.8%
 
p7132.3%
 
l6822.2%
 
h6512.1%
 
g6512.1%
 
I6512.1%
 
x6512.1%
 
H6512.1%
 
14341.4%
 
u4341.4%
 
34341.4%
 
y4341.4%
 
f4341.4%
 
D4341.4%
 
Other values (5)7132.3%
 

Claim Type
Categorical

Distinct8
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
Carrier
93 
Hospice
93 
Durable Medical Equipment
93 
Skilled Nursing Facility
93 
Inpatient
93 
Other values (3)
217 
ValueCountFrequency (%) 
Carrier9313.6%
 
Hospice9313.6%
 
Durable Medical Equipment9313.6%
 
Skilled Nursing Facility9313.6%
 
Inpatient9313.6%
 
Outpatient9313.6%
 
Home Health Agency9313.6%
 
Total314.5%
 
2020-12-12T17:08:53.102080image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T17:08:53.149621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:53.225186image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length10
Mean length13.86363636
Min length5

Overview of Unicode Properties

Unique unicode characters33
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e102310.8%
 
i9309.8%
 
t7758.2%
 
a6827.2%
 
l5896.2%
 
n5585.9%
 
5585.9%
 
r4654.9%
 
p3723.9%
 
u3723.9%
 
c3723.9%
 
H2793.0%
 
o2172.3%
 
d1862.0%
 
m1862.0%
 
s1862.0%
 
g1862.0%
 
y1862.0%
 
I931.0%
 
O931.0%
 
D931.0%
 
b931.0%
 
M931.0%
 
E931.0%
 
q931.0%
 
Other values (8)6827.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter765781.0%
 
Uppercase Letter124013.1%
 
Space Separator5585.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
H27922.5%
 
I937.5%
 
O937.5%
 
D937.5%
 
M937.5%
 
E937.5%
 
C937.5%
 
A937.5%
 
S937.5%
 
N937.5%
 
F937.5%
 
T312.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e102313.4%
 
i93012.1%
 
t77510.1%
 
a6828.9%
 
l5897.7%
 
n5587.3%
 
r4656.1%
 
p3724.9%
 
u3724.9%
 
c3724.9%
 
o2172.8%
 
d1862.4%
 
m1862.4%
 
s1862.4%
 
g1862.4%
 
y1862.4%
 
b931.2%
 
q931.2%
 
h931.2%
 
k931.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
558100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin889794.1%
 
Common5585.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e102311.5%
 
i93010.5%
 
t7758.7%
 
a6827.7%
 
l5896.6%
 
n5586.3%
 
r4655.2%
 
p3724.2%
 
u3724.2%
 
c3724.2%
 
H2793.1%
 
o2172.4%
 
d1862.1%
 
m1862.1%
 
s1862.1%
 
g1862.1%
 
y1862.1%
 
I931.0%
 
O931.0%
 
D931.0%
 
b931.0%
 
M931.0%
 
E931.0%
 
q931.0%
 
C931.0%
 
Other values (7)5896.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
558100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII9455100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e102310.8%
 
i9309.8%
 
t7758.2%
 
a6827.2%
 
l5896.2%
 
n5585.9%
 
5585.9%
 
r4654.9%
 
p3723.9%
 
u3723.9%
 
c3723.9%
 
H2793.0%
 
o2172.3%
 
d1862.0%
 
m1862.0%
 
s1862.0%
 
g1862.0%
 
y1862.0%
 
I931.0%
 
O931.0%
 
D931.0%
 
b931.0%
 
M931.0%
 
E931.0%
 
q931.0%
 
Other values (8)6827.2%
 

Avg Spending Per Episode (Hospital)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct363
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1653.651026
Minimum0
Maximum24579
Zeros198
Zeros (%)29.0%
Memory size5.5 KiB
2020-12-12T17:08:53.297247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median73.5
Q3828.5
95-th percentile10905.6
Maximum24579
Range24579
Interquartile range (IQR)828.5

Descriptive statistics

Standard deviation4221.626387
Coefficient of variation (CV)2.552912507
Kurtosis11.91551389
Mean1653.651026
Median Absolute Deviation (MAD)73.5
Skewness3.459083769
Sum1127790
Variance17822129.36
MonotocityNot monotonic
2020-12-12T17:08:53.378317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
019829.0%
 
2142.1%
 
2181.2%
 
181.2%
 
571.0%
 
850.7%
 
1550.7%
 
350.7%
 
1940.6%
 
740.6%
 
2240.6%
 
940.6%
 
1740.6%
 
440.6%
 
1640.6%
 
2340.6%
 
11330.4%
 
2030.4%
 
41430.4%
 
3730.4%
 
40130.4%
 
1330.4%
 
1430.4%
 
4220.3%
 
6720.3%
 
Other values (338)37555.0%
 
ValueCountFrequency (%) 
019829.0%
 
181.2%
 
2142.1%
 
350.7%
 
440.6%
 
571.0%
 
620.3%
 
740.6%
 
850.7%
 
940.6%
 
ValueCountFrequency (%) 
2457910.1%
 
2372710.1%
 
2349410.1%
 
2286810.1%
 
2274310.1%
 
2219910.1%
 
2135510.1%
 
2128010.1%
 
2061610.1%
 
2020410.1%
 

Avg Spending Per Episode (State)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1863.5
Minimum0
Maximum20499
Zeros124
Zeros (%)18.2%
Memory size5.5 KiB
2020-12-12T17:08:53.449378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median120.5
Q3939
95-th percentile10176
Maximum20499
Range20499
Interquartile range (IQR)938

Descriptive statistics

Standard deviation4617.102107
Coefficient of variation (CV)2.477650715
Kurtosis9.673401044
Mean1863.5
Median Absolute Deviation (MAD)120.5
Skewness3.230476485
Sum1270907
Variance21317631.87
MonotocityNot monotonic
2020-12-12T17:08:53.505427image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
012418.2%
 
1629.1%
 
20499314.5%
 
3520314.5%
 
192314.5%
 
126314.5%
 
432314.5%
 
12314.5%
 
4314.5%
 
760314.5%
 
10176314.5%
 
115314.5%
 
28314.5%
 
16314.5%
 
939314.5%
 
1464314.5%
 
644314.5%
 
2068314.5%
 
ValueCountFrequency (%) 
012418.2%
 
1629.1%
 
4314.5%
 
12314.5%
 
16314.5%
 
28314.5%
 
115314.5%
 
126314.5%
 
192314.5%
 
432314.5%
 
ValueCountFrequency (%) 
20499314.5%
 
10176314.5%
 
3520314.5%
 
2068314.5%
 
1464314.5%
 
939314.5%
 
760314.5%
 
644314.5%
 
432314.5%
 
192314.5%
 

Avg Spending Per Episode (Nation)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1779.818182
Minimum0
Maximum19578
Zeros124
Zeros (%)18.2%
Memory size5.5 KiB
2020-12-12T17:08:53.565979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median110
Q31078
95-th percentile8997
Maximum19578
Range19578
Interquartile range (IQR)1076

Descriptive statistics

Standard deviation4349.52081
Coefficient of variation (CV)2.443800639
Kurtosis10.22763729
Mean1779.818182
Median Absolute Deviation (MAD)110
Skewness3.296840001
Sum1213836
Variance18918331.27
MonotocityNot monotonic
2020-12-12T17:08:53.628032image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
012418.2%
 
1511314.5%
 
1314.5%
 
107314.5%
 
488314.5%
 
9314.5%
 
5314.5%
 
664314.5%
 
119314.5%
 
2314.5%
 
759314.5%
 
23314.5%
 
13314.5%
 
8997314.5%
 
1078314.5%
 
2602314.5%
 
3087314.5%
 
19578314.5%
 
113314.5%
 
ValueCountFrequency (%) 
012418.2%
 
1314.5%
 
2314.5%
 
5314.5%
 
9314.5%
 
13314.5%
 
23314.5%
 
107314.5%
 
113314.5%
 
119314.5%
 
ValueCountFrequency (%) 
19578314.5%
 
8997314.5%
 
3087314.5%
 
2602314.5%
 
1511314.5%
 
1078314.5%
 
759314.5%
 
664314.5%
 
488314.5%
 
119314.5%
 

Percent of Spending (Hospital)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct317
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.090967742
Minimum0
Maximum100
Zeros201
Zeros (%)29.5%
Memory size5.5 KiB
2020-12-12T17:08:53.705599image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.395
Q34.7825
95-th percentile54.2185
Maximum100
Range100
Interquartile range (IQR)4.7825

Descriptive statistics

Standard deviation22.6226669
Coefficient of variation (CV)2.488477305
Kurtosis9.489347422
Mean9.090967742
Median Absolute Deviation (MAD)0.395
Skewness3.201858997
Sum6200.04
Variance511.7850578
MonotocityNot monotonic
2020-12-12T17:08:53.787169image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
020129.5%
 
100314.5%
 
0.01202.9%
 
0.09101.5%
 
0.0391.3%
 
0.191.3%
 
0.0281.2%
 
0.0881.2%
 
0.0571.0%
 
0.0460.9%
 
0.1250.7%
 
0.0750.7%
 
0.1450.7%
 
0.1540.6%
 
0.0640.6%
 
0.1840.6%
 
0.340.6%
 
0.2440.6%
 
0.6530.4%
 
2.2330.4%
 
0.2830.4%
 
3.3230.4%
 
3.5530.4%
 
0.5320.3%
 
0.1920.3%
 
Other values (292)31946.8%
 
ValueCountFrequency (%) 
020129.5%
 
0.01202.9%
 
0.0281.2%
 
0.0391.3%
 
0.0460.9%
 
0.0571.0%
 
0.0640.6%
 
0.0750.7%
 
0.0881.2%
 
0.09101.5%
 
ValueCountFrequency (%) 
100314.5%
 
61.7710.1%
 
56.7210.1%
 
54.4510.1%
 
54.2610.1%
 
53.4310.1%
 
53.210.1%
 
52.9410.1%
 
51.9910.1%
 
51.8810.1%
 

Percent of Spending (State)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.090909091
Minimum0
Maximum100
Zeros155
Zeros (%)22.7%
Memory size5.5 KiB
2020-12-12T17:08:53.859231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.585
Q34.58
95-th percentile49.64
Maximum100
Range100
Interquartile range (IQR)4.57

Descriptive statistics

Standard deviation22.52328133
Coefficient of variation (CV)2.477560946
Kurtosis9.673801185
Mean9.090909091
Median Absolute Deviation (MAD)0.585
Skewness3.230538445
Sum6200
Variance507.2982018
MonotocityNot monotonic
2020-12-12T17:08:53.920284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
015522.7%
 
0.06314.5%
 
100314.5%
 
0.14314.5%
 
0.94314.5%
 
17.17314.5%
 
10.09314.5%
 
4.58314.5%
 
0.61314.5%
 
0.01314.5%
 
7.14314.5%
 
49.64314.5%
 
3.71314.5%
 
0.08314.5%
 
0.56314.5%
 
2.11314.5%
 
3.14314.5%
 
0.02314.5%
 
ValueCountFrequency (%) 
015522.7%
 
0.01314.5%
 
0.02314.5%
 
0.06314.5%
 
0.08314.5%
 
0.14314.5%
 
0.56314.5%
 
0.61314.5%
 
0.94314.5%
 
2.11314.5%
 
ValueCountFrequency (%) 
100314.5%
 
49.64314.5%
 
17.17314.5%
 
10.09314.5%
 
7.14314.5%
 
4.58314.5%
 
3.71314.5%
 
3.14314.5%
 
2.11314.5%
 
0.94314.5%
 

Percent of Spending (Nation)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.091818182
Minimum0
Maximum100
Zeros155
Zeros (%)22.7%
Memory size5.5 KiB
2020-12-12T17:08:53.981837image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.565
Q35.51
95-th percentile45.96
Maximum100
Range100
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation22.21665372
Coefficient of variation (CV)2.44358755
Kurtosis10.22661658
Mean9.091818182
Median Absolute Deviation (MAD)0.565
Skewness3.296688961
Sum6200.62
Variance493.5797024
MonotocityNot monotonic
2020-12-12T17:08:54.043390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
015522.7%
 
45.96314.5%
 
100314.5%
 
13.29314.5%
 
2.49314.5%
 
0.61314.5%
 
0.02314.5%
 
0.58314.5%
 
0.55314.5%
 
5.51314.5%
 
3.88314.5%
 
0.07314.5%
 
7.72314.5%
 
3.39314.5%
 
0.12314.5%
 
15.77314.5%
 
0.05314.5%
 
0.01314.5%
 
ValueCountFrequency (%) 
015522.7%
 
0.01314.5%
 
0.02314.5%
 
0.05314.5%
 
0.07314.5%
 
0.12314.5%
 
0.55314.5%
 
0.58314.5%
 
0.61314.5%
 
2.49314.5%
 
ValueCountFrequency (%) 
100314.5%
 
45.96314.5%
 
15.77314.5%
 
13.29314.5%
 
7.72314.5%
 
5.51314.5%
 
3.88314.5%
 
3.39314.5%
 
2.49314.5%
 
0.61314.5%
 

Measure Start Date
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
01/01/2013 12:00:00 AM
682 
ValueCountFrequency (%) 
01/01/2013 12:00:00 AM682100.0%
 
2020-12-12T17:08:54.104943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T17:08:54.142475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:54.183010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length22
Mean length22
Min length22

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0477431.8%
 
1272818.2%
 
/13649.1%
 
213649.1%
 
13649.1%
 
:13649.1%
 
36824.5%
 
A6824.5%
 
M6824.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number954863.6%
 
Other Punctuation272818.2%
 
Space Separator13649.1%
 
Uppercase Letter13649.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0477450.0%
 
1272828.6%
 
2136414.3%
 
36827.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/136450.0%
 
:136450.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1364100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A68250.0%
 
M68250.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1364090.9%
 
Latin13649.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0477435.0%
 
1272820.0%
 
/136410.0%
 
2136410.0%
 
136410.0%
 
:136410.0%
 
36825.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A68250.0%
 
M68250.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII15004100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0477431.8%
 
1272818.2%
 
/13649.1%
 
213649.1%
 
13649.1%
 
:13649.1%
 
36824.5%
 
A6824.5%
 
M6824.5%
 

Measure End Date
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
12/31/2013 12:00:00 AM
682 
ValueCountFrequency (%) 
12/31/2013 12:00:00 AM682100.0%
 
2020-12-12T17:08:54.244062image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T17:08:54.282095image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:54.322630image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length22
Mean length22
Min length22

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0341022.7%
 
1272818.2%
 
2204613.6%
 
/13649.1%
 
313649.1%
 
13649.1%
 
:13649.1%
 
A6824.5%
 
M6824.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number954863.6%
 
Other Punctuation272818.2%
 
Space Separator13649.1%
 
Uppercase Letter13649.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0341035.7%
 
1272828.6%
 
2204621.4%
 
3136414.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/136450.0%
 
:136450.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1364100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A68250.0%
 
M68250.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1364090.9%
 
Latin13649.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0341025.0%
 
1272820.0%
 
2204615.0%
 
/136410.0%
 
3136410.0%
 
136410.0%
 
:136410.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A68250.0%
 
M68250.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII15004100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0341022.7%
 
1272818.2%
 
2204613.6%
 
/13649.1%
 
313649.1%
 
13649.1%
 
:13649.1%
 
A6824.5%
 
M6824.5%
 

Location 1
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
UT (39.36070123200005, -111.58712831499997)
682 
ValueCountFrequency (%) 
UT (39.36070123200005, -111.58712831499997)682100.0%
 
2020-12-12T17:08:54.382682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T17:08:54.420714image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:54.463251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length43
Median length43
Mean length43
Min length43

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0409214.0%
 
1409214.0%
 
9341011.6%
 
327289.3%
 
720467.0%
 
220467.0%
 
.13644.7%
 
513644.7%
 
813644.7%
 
U6822.3%
 
T6822.3%
 
6822.3%
 
(6822.3%
 
66822.3%
 
,6822.3%
 
6822.3%
 
-6822.3%
 
46822.3%
 
)6822.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number2250676.7%
 
Other Punctuation20467.0%
 
Uppercase Letter13644.7%
 
Control6822.3%
 
Open Punctuation6822.3%
 
Space Separator6822.3%
 
Dash Punctuation6822.3%
 
Close Punctuation6822.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
U68250.0%
 
T68250.0%
 

Most frequent Control characters

ValueCountFrequency (%) 
682100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(682100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0409218.2%
 
1409218.2%
 
9341015.2%
 
3272812.1%
 
720469.1%
 
220469.1%
 
513646.1%
 
813646.1%
 
66823.0%
 
46823.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.136466.7%
 
,68233.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
682100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-682100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)682100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2796295.3%
 
Latin13644.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
U68250.0%
 
T68250.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0409214.6%
 
1409214.6%
 
9341012.2%
 
327289.8%
 
720467.3%
 
220467.3%
 
.13644.9%
 
513644.9%
 
813644.9%
 
6822.4%
 
(6822.4%
 
66822.4%
 
,6822.4%
 
6822.4%
 
-6822.4%
 
46822.4%
 
)6822.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII29326100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0409214.0%
 
1409214.0%
 
9341011.6%
 
327289.3%
 
720467.0%
 
220467.0%
 
.13644.7%
 
513644.7%
 
813644.7%
 
U6822.3%
 
T6822.3%
 
6822.3%
 
(6822.3%
 
66822.3%
 
,6822.3%
 
6822.3%
 
-6822.3%
 
46822.3%
 
)6822.3%
 

Interactions

2020-12-12T17:08:48.620223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:48.698290image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:48.773855image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:48.847418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:48.929489image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:49.004053image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:49.078117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:49.154683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:49.229247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:49.302810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:49.375373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:49.452940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:49.525502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:49.597564image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:49.669626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:49.741688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:49.812248image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:49.881808image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:49.957373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:50.026933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:50.096993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:50.167054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:50.248124image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:50.328192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:50.407261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:50.490833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:50.569400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:50.647467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:50.726535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:50.800099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:50.872661image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:50.944223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:51.021289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:51.092851image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:51.164412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:51.234473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:51.307035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:51.380598image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:51.451659image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:51.528726image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:51.599787image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:51.670347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:51.741409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:51.813971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:51.888535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:51.960097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:52.038664image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:52.111227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:52.184290image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T17:08:54.520801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T17:08:54.632396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T17:08:54.743492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T17:08:54.860593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T17:08:54.974691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T17:08:52.330416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T17:08:52.496058image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

Hospital NameProvider NumberPeriodClaim TypeAvg Spending Per Episode (Hospital)Avg Spending Per Episode (State)Avg Spending Per Episode (Nation)Percent of Spending (Hospital)Percent of Spending (State)Percent of Spending (Nation)Measure Start DateMeasure End DateLocation 1
0GARFIELD MEMORIAL HOSPITAL4600331 through 30 days After Discharge from Index Hospital AdmissionInpatient968.02068.02602.08.8610.0913.2901/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
1JORDAN VALLEY MEDICAL CENTER4600511 to 3 days Prior to Index Hospital AdmissionOutpatient35.0192.0113.00.180.940.5801/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
2LOGAN REGIONAL HOSPITAL4600151 through 30 days After Discharge from Index Hospital AdmissionDurable Medical Equipment105.0126.0107.00.620.610.5501/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
3ST MARKS HOSPITAL460047During Index Hospital AdmissionHospice0.00.00.00.000.000.0001/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
4OGDEN REGIONAL MEDICAL CENTER4600051 to 3 days Prior to Index Hospital AdmissionCarrier498.0432.0488.02.422.112.4901/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
5VALLEY VIEW MEDICAL CENTER4600071 through 30 days After Discharge from Index Hospital AdmissionHome Health Agency793.0939.0759.04.974.583.8801/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
6JORDAN VALLEY MEDICAL CENTER4600511 to 3 days Prior to Index Hospital AdmissionDurable Medical Equipment19.012.09.00.100.060.0501/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
7LDS HOSPITAL460006During Index Hospital AdmissionHome Health Agency0.00.00.00.000.000.0001/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
8LOGAN REGIONAL HOSPITAL460015During Index Hospital AdmissionHospice0.00.00.00.000.000.0001/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
9SEVIER VALLEY MEDICAL CENTER460026During Index Hospital AdmissionCarrier815.01464.01511.04.607.147.7201/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)

Last rows

Hospital NameProvider NumberPeriodClaim TypeAvg Spending Per Episode (Hospital)Avg Spending Per Episode (State)Avg Spending Per Episode (Nation)Percent of Spending (Hospital)Percent of Spending (State)Percent of Spending (Nation)Measure Start DateMeasure End DateLocation 1
672GARFIELD MEMORIAL HOSPITAL460033Complete EpisodeTotal10916.020499.019578.0100.00100.00100.0001/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
673INTERMOUNTAIN MEDICAL CENTER460010During Index Hospital AdmissionInpatient10502.010176.08997.049.3549.6445.9601/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
674GARFIELD MEMORIAL HOSPITAL4600331 to 3 days Prior to Index Hospital AdmissionCarrier205.0432.0488.01.872.112.4901/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
675JORDAN VALLEY MEDICAL CENTER460051Complete EpisodeTotal19266.020499.019578.0100.00100.00100.0001/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
676INTERMOUNTAIN MEDICAL CENTER4600101 through 30 days After Discharge from Index Hospital AdmissionOutpatient713.0644.0664.03.353.143.3901/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
677OGDEN REGIONAL MEDICAL CENTER4600051 to 3 days Prior to Index Hospital AdmissionHome Health Agency9.016.013.00.050.080.0701/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
678OGDEN REGIONAL MEDICAL CENTER4600051 to 3 days Prior to Index Hospital AdmissionDurable Medical Equipment5.012.09.00.020.060.0501/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
679LAKEVIEW HOSPITAL4600421 through 30 days After Discharge from Index Hospital AdmissionDurable Medical Equipment37.0126.0107.00.190.610.5501/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
680BEAR RIVER VALLEY HOSPITAL460039During Index Hospital AdmissionSkilled Nursing Facility0.00.00.00.000.000.0001/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)
681MOUNTAIN WEST MEDICAL CENTER4600141 through 30 days After Discharge from Index Hospital AdmissionHospice52.0115.0119.00.350.560.6101/01/2013 12:00:00 AM12/31/2013 12:00:00 AMUT\n(39.36070123200005, -111.58712831499997)